Our approach is inspired by the information bottleneck (IB) theory [1]}, where the learned latent representation \(Z\)  needs to make a consistent prediction with the ground-truth labels \(Y\)  while simultaneously contains the least mutual information \(I(X,Z)\)  with the given input \(X\) . In our framework, the information bottleneck is employed to compact the complicated latent representations to facilitate the feature alignment and adversarial training.
